How to Make Sure Your AI Project Succeeds

If you’ve struggled to get an AI project off
the ground, you’re not alone. A report published earlier this year by Databricks
found that just one in three AI initiatives are considered successful. The
survey, conducted by CIO/IDG Research Services, also found that it typically
takes over six months for a project to proceed from development to production.

Despite the long lead time and low success
rate, nearly 90 percent of the companies surveyed in the Databricks report say
they’re investing in AI solutions. That’s because they understand its
potential, which is enormous. The executives surveyed named predictive
analytics, automation and customer analytics as their top three reasons for
deploying AI.

AI can be a game-changer in those use cases and
many others. But many AI project leaders will encounter difficulties that stem
from an insufficient understanding of how AI works and what it can do. They
know AI is necessary for business success, but they think it’s sufficient in
and of itself. For all its potential and capabilities, AI is not an
out-of-the-box business problem solver — project leaders need a realistic
implementation strategy to generate returns quickly.

With the right strategy, it’s possible to
successfully implement an AI solution and accelerate the time to value. The
three-part A.C.T. strategy can help you solve the business problem that prompted
you to seek an AI solution in the first place — and extend the value AI
provides across your organization. Here’s how it works:

Analytical Framework: For AI to fulfill its promise, it needs an
analytical framework in which to operate. You can create an analytical
framework by identifying a basic business problem to solve and the data you’d need
to address it. Providing data is essential; according to the Databricks report,
“nearly all respondents (96%) cited multiple data-related challenges when it
comes time to move projects to production.”

Say you work for a CPG company selling
breakfast cereals to national grocery chains. Your business problem is this: Which
SKUs should you offer to which chains to reach your assertive growth goals? The
data you have on hand includes store information, past sales, upcoming
promotions and shelf placement. That forms your current analytical framework, which might apply this data to create store
segments, recommended SKU mixes, and the sell-in offer. That framework is also a
great start for a fast AI implementation. AI will build on the framework,
layering in more complex data and more intricate decision criteria, generating
self-learning algorithms. When this happens, store segments become dynamic and
include deeper data types. SKU mix recommendations are more in tune with
emerging market trends. And your assertive sell-in proposals are more likely to
be signed.

Context: Data alone won’t make AI all-knowing; you also have to provide
business context, especially if the datasets you’re using don’t provide enough
information for your AI solution to infer context. You have to input business
rules so that your AI solution factors them in when making recommendations. For
instance, salespeople know that new products are typically more attractive to
customers than existing lines, but AI doesn’t know that until you tell it. Without
that context, it may make SKU recommendations that favor past bestsellers.

Similarly, AI won’t factor in
important context like customer budgets, price point details, etc., unless you
feed that information into your AI solution. AI is capable of learning, so its
recommendations get sharper over time. But to start generating returns quickly,
it’s essential to provide context relevant to your business.

Technology: Using the right technology is crucial to the success of your AI
project. But because IT and analytics teams tend to operate separately, many
companies use traditional technology for their AI implementation. That can
cause them to lose out on one of the most important advantages AI can deliver:
the ability to extend value throughout the organization.

Using scalable technology is a better approach.
You should also make sure you use AI-compatible solutions that are already in
use in your company, like Salesforce. Rather than building solutions in-house,
consider technologies created by the AI community. And keep in mind that since
point solutions aren’t scalable, they are by definition of limited use. A
scalable platform is your best bet.

With the A.C.T. strategy, you can beat the odds
and make sure your AI project succeeds. By using this approach, you can also
generate value more rapidly, and since AI continuously learns, the fast start
will keep your company ahead of the curve. Using AI with A.C.T. can also allow
you to leverage AI’s amazing learning capacity across your organization,
extending the value beyond the initial business challenge. That’s the best way
to make sure your AI project succeeds.

About Anil Kaul

Anil has
over 22 years of experience in advanced analytics, market research, and
management consulting. He is very passionate about analytics and leveraging
technology to improve business decision-making. Prior to founding Absolutdata,
Anil worked at McKinsey & Co. and Personify. He is also on the board of
Edutopia, an innovative start-up in the language learning space.

An
in-demand writer and speaker, Anil has published articles in McKinsey
Quarterly, Marketing Science, Journal of Marketing Research and International
Journal of Research. He was recently listed among the ‘10 Most Influential
Analytics Leaders in India’ by Analytics Magazine India and has been quoted as
a “Game Changer” in Research World. Anil has spoken at many industry
conferences and top business schools, including Dartmouth, Berkeley, Cornell,
Yale, Columbia and New York University.